# pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models

arXiv: https://arxiv.org/abs/2105.00385 · HTML: https://mlsanity.com/paper/2105.00385/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 30 references that are in the map.
- First posted on arXiv 2 May 2021.

## Builds on (most cited first, 1 of 1)

- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)

## Built on by (most cited first, 13 of 13)

- [Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs](https://mlsanity.com/paper/2409.16490/) (2024)
- [Leveraging LLM-Respondents for Item Evaluation: a Psychometric Analysis](https://mlsanity.com/paper/2407.10899/) (2024)
- [Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks](https://mlsanity.com/paper/2206.03545/) (2022)
- [Pattern-based Knowledge Component Extraction from Student Code Using Representation Learning](https://mlsanity.com/paper/2508.09281/) (2025)
- [Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiency](https://mlsanity.com/paper/2311.08594/) (2023)
- [Assessing the Performance of Online Students -- New Data, New Approaches, Improved Accuracy](https://mlsanity.com/paper/2109.01753/) (2021)
- [Uncertainty-preserving deep knowledge tracing with state-space models](https://mlsanity.com/paper/2407.17427/) (2024)
- [Hierarchical Multi-Armed Bandits for the Concurrent Intelligent Tutoring of Concepts and Problems of Varying Difficulty Levels](https://mlsanity.com/paper/2408.07208/) (2024)
- [CogGen: A Learner-Centered Generative AI Architecture for Intelligent Tutoring with Programming Video](https://mlsanity.com/paper/2506.20600/) (2025)
- [Transferable Student Performance Modeling for Intelligent Tutoring Systems](https://mlsanity.com/paper/2202.03980/) (2022)
- [Analysis of Knowledge Tracing performance on synthesised student data](https://mlsanity.com/paper/2401.16832/) (2024)
- [Measuring the Impact of Student Gaming Behaviors on Learner Modeling](https://mlsanity.com/paper/2512.18659/) (2025)
- [UNVaMP: Neural Knowledge Tracing with Variational Regularization of Latent Knowledge Dynamics](https://mlsanity.com/paper/2608.03811/) (2026)

## Abstract (arXiv)

Bayesian Knowledge Tracing, a model used for cognitive mastery estimation, has been a hallmark of adaptive learning research and an integral component of deployed intelligent tutoring systems (ITS). In this paper, we provide a brief history of knowledge tracing model research and introduce pyBKT, an accessible and computationally efficient library of model extensions from the literature. The library provides data generation, fitting, prediction, and cross-validation routines, as well as a simple to use data helper interface to ingest typical tutor log dataset formats. We evaluate the runtime with various dataset sizes and compare to past implementations. Additionally, we conduct sanity checks of the model using experiments with simulated data to evaluate the accuracy of its EM parameter learning and use real-world data to validate its predictions, comparing pyBKT's supported model variants with results from the papers in which they were originally introduced. The library is open source and open license for the purpose of making knowledge tracing more accessible to communities of research and practice and to facilitate progress in the field through easier replication of past approaches.